featuretools.primitives.CityblockDistance#
- class featuretools.primitives.CityblockDistance(unit='miles')[source]#
Calculates the distance between points in a city road grid.
- Description:
This distance is calculated using the haversine formula, which takes into account the curvature of the Earth. If either input data contains NaN`s, the calculated distance with be `NaN. This calculation is also known as the Mahnattan distance.
- Parameters:
unit (str) – Determines the unit value to output. Could be miles or kilometers. Default is miles.
Examples
>>> cityblock_distance = CityblockDistance() >>> DC = (38, -77) >>> Boston = (43, -71) >>> NYC = (40, -74) >>> distances_mi = cityblock_distance([DC, DC], [NYC, Boston]) >>> np.round(distances_mi, 3).tolist() [301.519, 672.089]
We can also change the units in which the distance is calculated.
>>> cityblock_distance_kilometers = CityblockDistance(unit='kilometers') >>> distances_km = cityblock_distance_kilometers([DC, DC], [NYC, Boston]) >>> np.round(distances_km, 3).tolist() [485.248, 1081.622]
Methods
__init__
([unit])flatten_nested_input_types
(input_types)Flattens nested column schema inputs into a single list.
generate_name
(base_feature_names)generate_names
(base_feature_names)get_args_string
()get_arguments
()get_description
(input_column_descriptions[, ...])get_filepath
(filename)get_function
()Attributes
base_of
base_of_exclude
commutative
compatibility
Additional compatible libraries
default_value
Default value this feature returns if no data found.
description_template
input_types
woodwork.ColumnSchema types of inputs
max_stack_depth
name
Name of the primitive
number_output_features
Number of columns in feature matrix associated with this feature
return_type
ColumnSchema type of return
series_library
stack_on
stack_on_exclude
stack_on_self
uses_calc_time
uses_full_dataframe